SoroushVahidi/scidocs huggingface dataset:
Localization for mobile sensor networks
(2004)

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
Taxonomy and Survey of Collaborative Intrusion Detection
(2015)

The dependency of our society on networked computers has become frightening: In the economy, all-digital networks have turned from facilitators to drivers; as cyber-physical systems are coming of age, computer networks are now becoming the central nervous systems of our physical world—even of highly critical infrastructures such as the power grid. At the same time, the 24/7 availability and correct functioning of networked computers has become much more threatened: The number of sophisticated and highly tailored attacks on IT systems has significantly increased. Intrusion Detection Systems (IDSs) are a key component of the corresponding defense measures; they have been extensively studied and utilized in the past. Since conventional IDSs are not scalable to big company networks and beyond, nor to massively parallel attacks, Collaborative IDSs (CIDSs) have emerged. They consist of several monitoring components that collect and exchange data. Depending on the specific CIDS architecture, central or distributed analysis components mine the gathered data to identify attacks. Resulting alerts are correlated among multiple monitors in order to create a holistic view of the network monitored. This article first determines relevant requirements for CIDSs; it then differentiates distinct building blocks as a basis for introducing a CIDS design space and for discussing it with respect to requirements. Based on this design space, attacks that evade CIDSs and attacks on the availability of the CIDSs themselves are discussed. The entire framework of requirements, building blocks, and attacks as introduced is then used for a comprehensive analysis of the state of the art in collaborative intrusion detection, including a detailed survey and comparison of specific CIDS approaches.
Confabulation based sentence completion for machine reading
(2011)

Sentence completion and prediction refers to the capability of filling missing words in any incomplete sentences. It is one of the keys to reading comprehension, thus making sentence completion an indispensible component of machine reading. Cogent confabulation is a bio-inspired computational model that mimics the human information processing. The building of confabulation knowledge base uses an unsupervised machine learning algorithm that extracts the relations between objects at the symbolic level. In this work, we propose performance improved training and recall algorithms that apply the cogent confabulation model to solve the sentence completion problem. Our training algorithm adopts a two-level hash table, which significantly improves the training speed, so that a large knowledge base can be built at relatively low computation cost. The proposed recall function fills missing words based on the sentence context. Experimental results show that our software can complete trained sentences with 100% accuracy. It also gives semantically correct answers to more than two thirds of the testing sentences that have not been trained before.
Seismic Data Classification Using Machine Learning
(2018)

Earthquakes around the world have been a cause of major destruction and loss of life and property. An early detection and prediction system using machine learning classification models can prove to be very useful for disaster management teams. The earthquake stations continuously collect data even when there is no event. From this data, we need to distinguish earthquake and non-earthquake. Machine learning techniques can be used to analyze continuous time series data to detect earthquakes effectively. Furthermore, the earthquake data can be used to predict the P-wave and S-wave arrival times.
Learning Compact Appearance Representation for Video-based Person Re-Identification
(2017)

This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs). Unlike previous work, we intend to extract a compact yet discriminative appearance representation from several frames rather than the whole sequence. Specifically, given a video, the representative frames are selected based on the walking profile of consecutive frames. A multiple CNN architecture incorporated with feature pooling is proposed to learn and compile the features of the selected representative frames into a compact description about the pedestrian for identification. Experiments are conducted on benchmark datasets to demonstrate the superiority of the proposed method over existing person re-identification approaches.
Extracting Social Network and Character Categorization From Bengali Literature
(2018)

Literature network analysis is an emerging area in the computational research domain. Literature network is a type of social network with various distinct features. The analysis explores significance of human behavior and complex social relationships. The story consists of some characters and creates an interconnected social system. Each character of the literature represents a node and the edge between any two nodes offered the interaction between them. An annotation and a novel character categorization method are developed to extract interactive social network from the Bengali drama. We analyze Raktakarabi and Muktodhara, two renowned Bengali dramas of Rabindranath Tagore. Weighted degree, closeness, and betweenness centrality analyze the correlation among the characters. We propose an edge contribution-based centrality and diversity metric of a node to determine the influence of one character over others. High diverse nodes show low clustering coefficient and vice versa. We propose a novel idea to analyze the characteristics of protagonist and antagonist from the influential nodes based on the complex graph. We also present a game theory-based community detection method that clusters the actors with a high degree of relationship. Evaluation on real-world networks demonstrates the superiority of the proposed method over the other existing algorithms. Interrelationship of the actors within the drama is also shown from the detected communities, as underlying theme of the narrations is identical. The analytical results show that our method efficiently finds the protagonist and antagonist from the literature network. The method is unique, and the analytical results are more accurate and unbiased than the human perspective. Our approach establishes similar results compared with the benchmark analysis available in Tagore’s Bengali literature.
Fast Feature Pyramids for Object Detection
(2014)

Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).
Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces
(2013)

We propose a novel and fast multiscale feature detection and description approach that exploits the benefits of nonlinear scale spaces. Previous attempts to detect and describe features in nonlinear scale spaces are highly time consuming due to the computational burden of creating the nonlinear scale space. In this paper we propose to use recent numerical schemes called Fast Explicit Diffusion (FED) embedded in a pyramidal framework to dramatically speed-up feature detection in nonlinear scale spaces. In addition, we introduce a Modified-Local Difference Binary (M-LDB) descriptor that is highly efficient, exploits gradient information from the nonlinear scale space, is scale and rotation invariant and has low storage requirements. We present an extensive evaluation that shows the excellent compromise between speed and performance of our approach compared to state-of-the-art methods such as BRISK, ORB, SURF, SIFT and KAZE.
Polynomial Eigenvalue Solutions to the 5-pt and 6-pt Relative Pose Problems
(2008)

In this paper we provide new fast and simple solutions to two important minimal problems in computer vision, the five-point relative pose problem and the six-point focal length problem. We show that these two problems can easily be formulated as polynomial eigenvalue problems of degree three and two and solved using standard efficient numerical algorithms. Our solutions are somewhat more stable than state-of-the-art solutions by Nister and Stewenius and are in some sense more straightforward and easier to implement since polynomial eigenvalue problems are well studied with many efficient and robust algorithms available. The quality of the solvers is demonstrated in experiments 1.
Multi-image matching using multi-scale oriented patches
(2005)

This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 /spl times/ 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.
The Option-Critic Architecture
(2016)

We first studied the behavior of the intra-option policy gradient algorithm when the initiation sets and subgoals are fixed by hand. In this case, options terminate with probability 0.9 in a hallway state and four of its incoming neighboring states. We chose to parametrize the intra-option policies using the softmax distribution with a one-hot encoding of state-action pairs as basis functions. Learning both intra-option policies and terminations
Dark vs. Dim Silicon and Near-Threshold Computing
(2017)

Due to limited scaling of supply voltage, power density is expected to grow in future technology nodes. This increasing power density potentially limits the number of transistors switching at full speed in the future. Near-threshold operation can increase the number of simultaneously active cores, at the expense of much lower operating frequency (“dim silicon”). Although promising to increase overall throughput, dim cores suffer from diminishing returns as the number of cores increases. At this point, hardware accelerators become more efficient alternatives. To explore such a broad design space, we have developed a framework called Lumos to analytically quantify the performance limits of many-core, heterogeneous systems operating at near-threshold voltage. Lumos augments Amdahl’s Law with detailed scaling of frequency and power, calibrated by circuit-level simulations using a modified Predictive Technology Model (PTM) and factors in effects of process variations. While our results show that dim cores do indeed boost throughput, even in the presence of process variations, significant benefits are only achieved in applications with very high parallelism or with novel architectures to mitigate variation. A more beneficial 1 and scalable approach is to use accelerators. However, reconfigurable logic that supports a variety of accelerators is more beneficial than a dedicated, fixed-logic accelerator, unless 1) the dedicated kernel has overwhelming coverage across applications (e.g. twice as large as the total of all others), or 2) the speedup of the dedicated accelerator over the reconfigurable equivalent is significant (e.g. 10x-50x).
AmiGO: online access to ontology and annotation data
(2009)

AmiGO is a web application that allows users to query, browse and visualize ontologies and related gene product annotation (association) data. AmiGO can be used online at the Gene Ontology (GO) website to access the data provided by the GO Consortium; it can also be downloaded and installed to browse local ontologies and annotations. AmiGO is free open source software developed and maintained by the GO Consortium.
Stress and hippocampal plasticity.
(1999)

The hippocampus is a target of stress hormones, and it is an especially plastic and vulnerable region of the brain. It also responds to gonadal, thyroid, and adrenal hormones, which modulate changes in synapse formation and dendritic structure and regulate dentate gyrus volume during development and in adult life. Two forms of structural plasticity are affected by stress: Repeated stress causes atrophy of dendrites in the CA3 region, and both acute and chronic stress suppresses neurogenesis of dentate gyrus granule neurons. Besides glucocorticoids, excitatory amino acids and N-methyl-D-aspartate (NMDA) receptors are involved in these two forms of plasticity as well as in neuronal death that is caused in pyramidal neurons by seizures and by ischemia. The two forms of hippocampal structural plasticity are relevant to the human hippocampus, which undergoes a selective atrophy in a number of disorders, accompanied by deficits in declarative episodic, spatial, and contextual memory performance. It is important, from a therapeutic standpoint, to distinguish between a permanent loss of cells and a reversible atrophy.
Conservative treatment of carpal tunnel syndrome: comparison between laser therapy and Fascial Manipulation(®).
(2015)

The etiopathogenesis of Carpal Tunnel Syndrome (CTS) is multifactorial and most cases are classified as idiopathic (Thurston 2013). A randomized controlled trial was performed to compare the effectiveness of Fascial Manipulation(®) (FM) and Low-Level Laser Therapy (LLLT) for CTS. This prospective trial included 42 patients (70 hands with symptoms) with clinical and electroneuromyographic diagnosis of CTS. The patients were randomly assigned to receive multiple sessions of FM or multiple session of LLLT. The Visual Analogic Scale (VAS) and Boston Carpal Tunnel Questionnaire (BCTQ) were performed at baseline, end of treatment and after three months. The group that received FM showed a significant reduction in subjective pain perception and an increased function assessed by BCTQ at the end of the treatment and follow-up. The group that received LLLT showed an improvement in the BCTQ at the end of the treatment but the improvement level was not sustained at the three month follow-up. FM is a valid alternative treatment for CTS.
An OFDMA based multiple access protocol with QoS guarantee for next generation WLAN
(2015)

To provide better QoS guarantee for the next generation WLAN, IEEE 802.11ax task group is founded in March 2014. As a promising technology to accommodate multiple nodes concurrent transmissions in dense deployment scenario, orthogonal frequency division multiple access (OFDMA) will be adopted in IEEE 802.11ax with great possibility. In this paper, an OFDMA based multiple access protocol with QoS guarantee is proposed for the next generation WLAN. Firstly, a redundant access mechanism is given to increase the access success probability of the video traffic where the video stations can concurrently send multiple RTS packets in multiple subchannels. Secondly, a priority based resource allocation scheme is presented to let AP allocate more resources to the video stations. Simulation results show that our protocol outperforms the existing OFDMA based multiple access for IEEE 802.11ax (OMAX) protocol in terms of delay and delay jitter of video traffic in dense deployment scenario.
10.6 THz figure-of-merit phase-change RF switches with embedded micro-heater
(2015)

We report on GeTe-based phase-change RF switches with embedded micro-heater for thermal switching. With heater parasitics reduced, GeTe RF switches show onstate resistance of 0.05 ohm*mm and off-state capacitance of 0.3 pF/mm. The RF switch figure-of-merit is estimated to be 10.6 THz, which is about 15 times better than state-of-the-art silicon-on-insulator switches. With on-state resistance of 1 ohm and off-state capacitance of 15 fF, RF insertion loss was measured at <;0.2 dB, and isolation was >25 dB at 20 GHz, respectively. RF power handling was >5.6 W for both onand off-state of GeTe.
The Mediator complex: a central integrator of transcription
(2015)

The RNA polymerase II (Pol II) enzyme transcribes all protein-coding and most non-coding RNA genes and is globally regulated by Mediator — a large, conformationally flexible protein complex with a variable subunit composition (for example, a four-subunit cyclin-dependent kinase 8 module can reversibly associate with it). These biochemical characteristics are fundamentally important for Mediator's ability to control various processes that are important for transcription, including the organization of chromatin architecture and the regulation of Pol II pre-initiation, initiation, re-initiation, pausing and elongation. Although Mediator exists in all eukaryotes, a variety of Mediator functions seem to be specific to metazoans, which is indicative of more diverse regulatory requirements.
From information security to ... business security?
(2005)

This short opinion paper argues that information security, the discipline responsible for protecting a company's information assets against business risks, has now become such a crucial component of good Corporate Governance, that it should rather be called Business Security instead of Information Security. During the last year or two, driven by developments in the field of Corporate Governance, including IT Governance, it became apparent that the scope of Information Security is much wider than just (directly) protecting the data, information and software of a business. Such data, information and software had become invaluable assets of the business as a whole, and not properly protecting this information could have profound business and legal implications. Basically, the data and information of the business became its 'life blood', and compromising this life blood, could kill the business. Executive Management and Boards started realizing that Information Security Governance was becoming their direct responsibility, and that serious personal consequences, specifically legally, could flow from ignoring information security. Information security governance had become an
Support Vector Machines For Synthetic Aperture Radar Automatic Target Recognition
(2000)

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. This paper presents a real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) and compares the result with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local " bounded " decision region around each class that presents better rejection to confusers.
A SiC-Based High-Efficiency Isolated Onboard PEV Charger With Ultrawide DC-Link Voltage Range
(2017)

In LLC-based onboard battery charging architectures used in plug-in electric vehicles (PEV), the dc link voltage can be actively regulated to follow the battery pack voltage so that the LLC converter can operate in proximity of resonant frequency and achieve high efficiencies over the wide range of battery pack voltage. However, conventional boost-type power factor correction (PFC) converters are unable to provide ultrawide dc link voltages since their output voltages should always be larger than their input voltages. This paper proposes a Silicon Carbide (SiC)-based onboard PEV charger using single-ended primary-inductor converter (SEPIC) PFC converter followed by an isolated LLC resonant converter. With the proposed charger architecture, the SEPIC PFC converter is able to provide an ultrawide range for dc link voltage, and consequently enhance the efficiency of the LLC stage by ensuring operation in proximity of resonant frequency. A 1-kW SiC-based prototype is designed to validate the proposed idea. The experimental result shows that the SEPIC PFC converter achieves unity power factor, 2.72% total harmonic distortion, and 95.3% peak conversion efficiency. The LLC  converter achieves 97.1% peak efficiency and always demonstrates a very high efficiency across the ultrawide dc-link voltage range. The overall efficiency of the charger is 88.5% to 93.5% from 20% of the rated load to full load.
EKG-based key agreement in Body Sensor Networks
(2008)

Preserving a person's privacy in an efficient manner is very important for critical, life-saving infrastructures like body sensor networks (BSN). This paper presents a novel key agreement scheme which allows two sensors in a BSN to agree to a common key generated using electrocardiogram (EKG) signals. This EKG-based key agreement (EKA) scheme aims to bring the "plug-n-play" paradigm to BSN security whereby simply deploying sensors on the subject can enable secure communication, without requiring any form of initialization such as pre-deployment. Analysis of the scheme based on real EKG data (obtained from MIT PhysioBank database) shows that keys resulting from EKA are: random, time variant, can be generated based on short-duration EKG measurements, identical for a given subject and different for separate individuals.